{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving Spectra from a Database [v1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "\n",
    "import specdb\n",
    "from specdb.specdb import SpecDB\n",
    "from specdb import specdb as spdb_spdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database is igmspec\n",
      "Created on 2017-Jan-07\n"
     ]
    }
   ],
   "source": [
    "db_file = specdb.__path__[0]+'/tests/files/IGMspec_DB_v02_debug.hdf5'\n",
    "reload(spdb_spdb)\n",
    "sdb = spdb_spdb.SpecDB(db_file=db_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectra from a Meta data Query\n",
    "    Returned spectra in the XSpectrum1D object are aligned to the Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform meta query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your search yielded 1 match[es] within radius=1 arcsec\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table length=1&gt;\n",
       "<table id=\"table4624456784\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SDSS_NAME</th><th>RA_GROUP</th><th>DEC_GROUP</th><th>THING_ID</th><th>PLATE</th><th>MJD</th><th>FIBERID</th><th>Z_VI</th><th>Z_PIPE</th><th>ERR_ZPIPE</th><th>ZWARNING</th><th>Z_PCA</th><th>ERR_ZPCA</th><th>PCA_QUAL</th><th>Z_CIV</th><th>Z_CIII</th><th>Z_MGII</th><th>SDSS_MORPHO</th><th>BOSS_TARGET1</th><th>ANCILLARY_TARGET1</th><th>ANCILLARY_TARGET2</th><th>EBOSS_TARGET0</th><th>NSPEC_BOSS</th><th>PLATE_DUPLICATE [32]</th><th>MJD_DUPLICATE [32]</th><th>FIBERID_DUPLICATE [32]</th><th>SDSS_DR7</th><th>PLATE_DR7</th><th>MJD_DR7</th><th>FIBERID_DR7</th><th>UNIFORM</th><th>ALPHA_NU</th><th>SNR_SPEC</th><th>SNR_DUPLICATE [32]</th><th>SNR_1700</th><th>SNR_3000</th><th>SNR_5150</th><th>FWHM_CIV</th><th>BHWHM_CIV</th><th>RHWHM_CIV</th><th>AMP_CIV</th><th>REWE_CIV</th><th>ERR_REWE_CIV</th><th>FWHM_CIII</th><th>BHWHM_CIII</th><th>RHWHM_CIII</th><th>AMP_CIII</th><th>REWE_CIII</th><th>ERR_REWE_CIII</th><th>FWHM_MGII</th><th>BHWHM_MGII</th><th>RHWHM_MGII</th><th>AMP_MGII</th><th>REWE_MGII</th><th>ERR_REWE_MGII</th><th>BAL_FLAG_VI</th><th>BI_CIV</th><th>ERR_BI_CIV</th><th>AI_CIV</th><th>ERR_AI_CIV</th><th>CHI2TROUGH</th><th>NCIV_2000</th><th>VMIN_CIV_2000</th><th>VMAX_CIV_2000</th><th>NCIV_450</th><th>VMIN_CIV_450</th><th>VMAX_CIV_450</th><th>REW_SIIV</th><th>REW_CIV</th><th>REW_ALIII</th><th>RUN_NUMBER</th><th>PHOTO_MJD</th><th>RERUN_NUMBER</th><th>COL_NUMBER</th><th>FIELD_NUMBER</th><th>OBJ_ID</th><th>PSFFLUX [5]</th><th>IVAR_PSFFLUX [5]</th><th>PSFMAG [5]</th><th>ERR_PSFMAG [5]</th><th>TARGET_FLUX [5]</th><th>MI</th><th>DGMI</th><th>EXTINCTION [5]</th><th>EXTINCTION_RECAL [5]</th><th>HI_GAL</th><th>VAR_MATCHED</th><th>VAR_CHI2</th><th>VAR_A</th><th>VAR_GAMMA</th><th>RASS_COUNTS</th><th>RASS_COUNTS_SNR</th><th>SDSS2ROSAT_SEP</th><th>N_DETECTION_XMM</th><th>FLUX02_12KEV_SGL</th><th>ERR_FLUX02_12KEV_SGL</th><th>FLUX02_2KEV</th><th>ERR_FLUX02_2KEV</th><th>FLUX2_12KEV</th><th>ERR_FLUX2_12KEV</th><th>FLUX02_12KEV</th><th>ERR_FLUX02_12KEV</th><th>LUM02_2KEV_SGL</th><th>LUM05_2KEV</th><th>LUM2_12KEV</th><th>LUM02_2KEV</th><th>LUMX2_10_UPPER</th><th>SDSS2XMM_SEP</th><th>GALEX_MATCHED</th><th>FUV</th><th>FUV_IVAR</th><th>NUV</th><th>NUV_IVAR</th><th>JMAG</th><th>ERR_JMAG</th><th>JSNR</th><th>JRDFLAG</th><th>HMAG</th><th>ERR_HMAG</th><th>HSNR</th><th>HRDFLAG</th><th>KMAG</th><th>ERR_KMAG</th><th>KSNR</th><th>KRDFLAG</th><th>SDSS2MASS_SEP</th><th>W1MAG</th><th>ERR_W1MAG</th><th>W1SNR</th><th>W1CHI2</th><th>W2MAG</th><th>ERR_W2MAG</th><th>W2SNR</th><th>W2CHI2</th><th>W3MAG</th><th>ERR_W3MAG</th><th>W3SNR</th><th>W3CHI2</th><th>W4MAG</th><th>ERR_W4MAG</th><th>W4SNR</th><th>W4CHI2</th><th>CC_FLAGS</th><th>PH_FLAG</th><th>SDSS2WISE_SEP</th><th>UKIDSS_MATCHED</th><th>YFLUX</th><th>YFLUX_ERR</th><th>JFLUX</th><th>JFLUX_ERR</th><th>HFLUX</th><th>HFLUX_ERR</th><th>KFLUX</th><th>KFLUX_ERR</th><th>FIRST_MATCHED</th><th>FIRST_FLUX</th><th>FIRST_SNR</th><th>SDSS2FIRST_SEP</th><th>DATE-OBS</th><th>R</th><th>zem_GROUP</th><th>sig_zem</th><th>flag_zem</th><th>STYPE</th><th>IGM_ID</th><th>SPEC_FILE</th><th>NPIX</th><th>WV_MIN</th><th>WV_MAX</th><th>GROUP_ID</th><th>EPOCH</th><th>CAT</th><th>INSTR</th><th>DISPERSER</th><th>TELESCOPE</th><th>GROUP</th></tr></thead>\n",
       "<thead><tr><th>str18</th><th>float64</th><th>float64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int32</th><th>int16</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int16</th><th>int32</th><th>str3</th><th>int16</th><th>int16</th><th>str19</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float32</th><th>float64</th><th>int16</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int16</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int16</th><th>float64</th><th>int16</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>int32</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str4</th><th>str4</th><th>float64</th><th>int16</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>float32</th><th>int16</th><th>float64</th><th>float64</th><th>float64</th><th>str10</th><th>float64</th><th>float64</th><th>float64</th><th>str9</th><th>str3</th><th>int64</th><th>str28</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>str5</th><th>str4</th><th>str4</th><th>str10</th><th>str9</th></tr></thead>\n",
       "<tr><td>000000.45+174625.4</td><td>0.0019</td><td>17.7737</td><td>268514930</td><td>6173</td><td>56238</td><td>528</td><td>2.30909729004</td><td>2.30909729004</td><td>0.000943339022342</td><td>0</td><td>2.30763868197</td><td>-1.0</td><td>0.346182279084</td><td>2.31230791362</td><td>2.30638494927</td><td>2.3046866469</td><td>0</td><td>2199023796224</td><td>0</td><td>0</td><td>0</td><td>0</td><td>-1 .. -1</td><td>-1 .. -1</td><td>-1 .. -1</td><td>0</td><td>-1</td><td>-1</td><td>-1</td><td>0</td><td>0.537336213179</td><td>0.779484925454</td><td>-1.0 .. -1.0</td><td>0.58568293642</td><td>0.915501904005</td><td>-1.0</td><td>4278.7900341</td><td>1595.2886496</td><td>2683.5013845</td><td>2.15328741853</td><td>44.0028643812</td><td>1.40097166147</td><td>3575.00108901</td><td>1554.12165803</td><td>2020.87943097</td><td>0.706666936497</td><td>38.4285671721</td><td>15.8517240349</td><td>7996.40388327</td><td>4579.19012749</td><td>3417.21375578</td><td>0.223279535625</td><td>69.3860000703</td><td>17.8808108042</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>7713</td><td>54741</td><td>301</td><td>4</td><td>231</td><td>1237678601842131080</td><td>1.03847694397 .. 2.65843176842</td><td>12.3596630096 .. 0.852828204632</td><td>22.4397926331 .. 21.3626842499</td><td>0.28713452816 .. 0.386405169964</td><td>1.03847694397 .. 2.65843176842</td><td>-23.882931269</td><td>-0.0820910409093</td><td>0.146194890141 .. 0.0419441759586</td><td>0.113614 .. 0.0338511</td><td>21.9297126138</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>1</td><td>0.302318871021</td><td>8.78557314038</td><td>0.727564871311</td><td>4.53735896492</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td></td><td></td><td>0.0</td><td>0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>-1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>2012-11-07</td><td>2100.0</td><td>2.308</td><td>-1.0</td><td>BOSS_PCA</td><td>QSO</td><td>0</td><td>spec-6173-56238-0528.fits.gz</td><td>4556</td><td>3605.0</td><td>10289.6</td><td>0</td><td>2000.0</td><td>DR12Q</td><td>BOSS</td><td>BOTH</td><td>SDSS 2.5-M</td><td>BOSS_DR12</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "    SDSS_NAME      RA_GROUP DEC_GROUP ... DISPERSER TELESCOPE    GROUP  \n",
       "      str18        float64   float64  ...    str4     str10       str9  \n",
       "------------------ -------- --------- ... --------- ---------- ---------\n",
       "000000.45+174625.4   0.0019   17.7737 ...      BOTH SDSS 2.5-M BOSS_DR12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = sdb.meta_from_position((0.0019,17.7737), 1*u.arcsec)\n",
    "meta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve spectra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staged 1 spectra totalling 9.3e-05 Gb\n",
      "Loaded spectra\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xavier/local/Python/linetools/linetools/spectra/xspectrum1d.py:248: UserWarning: No unit given to wavelength, assuming Angstroms.\n",
      "  warnings.warn(\"No unit given to wavelength, assuming Angstroms.\")\n"
     ]
    }
   ],
   "source": [
    "spec = sdb.spectra_from_meta(meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OpJi6zCObTAZe5kFEREQUV14s2mJHUWZaHXxXQpmHOpiOUr1TUWY6mQw8M01ERETyyrVs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\n9l3c5/i4WoaZae04xcTlspMSjY7ZMAIM14ytJ5DwMeSt6GB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dWMqRCnlZa6lxD/nBIJnW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qAAAg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\nz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rxKegT9rpTp+TaMg7JIhEnV8hSm6NzZJby5HezaYeVK+8j2vn16F5R33nmHBx98UP1b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\nGg2PPPLIVddtD6Jk2sLXqt97U09EWxLWluabg0y31HUyz/TWngLYOtwSm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D4xo9etW8fq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113a39e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spec.plot(inline=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple spectra for a single source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your search yielded 1 match[es] within radius=1 arcsec\n",
      "Staged 2 spectra totalling 6.4e-05 Gb\n",
      "Loaded spectra\n",
      "Nspec = 2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<XSpectrum1D: file=none, nspec=2, select=0, wvmin=4911.03 Angstrom, wvmax=7831.05 Angstrom>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = sdb.meta_from_position('001115.23+144601.8', 1*u.arcsec)\n",
    "spec = sdb.spectra_from_meta(meta)\n",
    "print('Nspec = {:d}'.format(spec.nspec))\n",
    "spec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectra for a Single Source\n",
    "    Performs the meta query and retrieves spectra in one call\n",
    "    Default tolerance on position is 0.5 arcseconds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat of multiple spectra request from above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your search yielded 1 match[es] within radius=0.5 arcsec\n",
      "Staged 2 spectra totalling 6.4e-05 Gb\n",
      "Loaded spectra\n"
     ]
    }
   ],
   "source": [
    "spec, meta = sdb.spectra_from_coord('001115.23+144601.8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=2&gt;\n",
       "<table id=\"table4679949520\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SDSSJ</th><th>RA_GROUP</th><th>DEC_GROUP</th><th>GROUP_ID</th><th>GROUP</th><th>IGM_ID</th><th>DISPERSER</th></tr></thead>\n",
       "<thead><tr><th>str31</th><th>float64</th><th>float64</th><th>int64</th><th>str3</th><th>int64</th><th>str4</th></tr></thead>\n",
       "<tr><td>001115.23+144601.8</td><td>2.8135</td><td>14.7672</td><td>0</td><td>GGG</td><td>3244</td><td>B600</td></tr>\n",
       "<tr><td>001115.23+144601.8</td><td>2.8135</td><td>14.7672</td><td>163</td><td>GGG</td><td>3244</td><td>R400</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "      SDSSJ        RA_GROUP DEC_GROUP GROUP_ID GROUP IGM_ID DISPERSER\n",
       "      str31        float64   float64   int64    str3 int64     str4  \n",
       "------------------ -------- --------- -------- ----- ------ ---------\n",
       "001115.23+144601.8   2.8135   14.7672        0   GGG   3244      B600\n",
       "001115.23+144601.8   2.8135   14.7672      163   GGG   3244      R400"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta[['SDSSJ','RA_GROUP','DEC_GROUP','GROUP_ID', 'GROUP', 'IGM_ID', 'DISPERSER']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One can restrict by group or meta data as in a Meta data Query\n",
    "    Note: The query is performed on both the source catalog and the meta data in each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key DISPERSER in query_dict is not present in Table catalog\n",
      "Your search yielded 1 match[es] within radius=0.5 arcsec\n",
      "Staged 1 spectra totalling 3.2e-05 Gb\n",
      "Loaded spectra\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table length=1&gt;\n",
       "<table id=\"table4680927824\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SDSSJ</th><th>RA_GROUP</th><th>DEC_GROUP</th><th>GROUP_ID</th><th>GROUP</th><th>IGM_ID</th><th>DISPERSER</th></tr></thead>\n",
       "<thead><tr><th>str31</th><th>float64</th><th>float64</th><th>int64</th><th>str3</th><th>int64</th><th>str4</th></tr></thead>\n",
       "<tr><td>001115.23+144601.8</td><td>2.8135</td><td>14.7672</td><td>163</td><td>GGG</td><td>3244</td><td>R400</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=1>\n",
       "      SDSSJ        RA_GROUP DEC_GROUP GROUP_ID GROUP IGM_ID DISPERSER\n",
       "      str31        float64   float64   int64    str3 int64     str4  \n",
       "------------------ -------- --------- -------- ----- ------ ---------\n",
       "001115.23+144601.8   2.8135   14.7672      163   GGG   3244      R400"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qdict = dict(DISPERSER='R400')\n",
    "spec, meta = sdb.spectra_from_coord('001115.23+144601.8', query_dict=qdict)\n",
    "meta[['SDSSJ','RA_GROUP','DEC_GROUP','GROUP_ID', 'GROUP', 'IGM_ID', 'DISPERSER']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If multiple sources are match to the input coordiantes (e.g. by increasing the tolerance), only the closest is taken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your search yielded 5 match[es] within radius=10 deg\n",
      "Staged 1 spectra totalling 9.3e-05 Gb\n",
      "Loaded spectra\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec, meta = sdb.spectra_from_coord((0.0019, 17.7737), tol=10*u.deg)\n",
    "len(meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the IDKEY instead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staged 2 spectra totalling 6.4e-05 Gb\n",
      "Loaded spectra\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table length=2&gt;\n",
       "<table id=\"table4629586192\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SDSSJ</th><th>RA_GROUP</th><th>DEC_GROUP</th><th>GROUP_ID</th><th>GROUP</th><th>IGM_ID</th><th>DISPERSER</th></tr></thead>\n",
       "<thead><tr><th>str31</th><th>float64</th><th>float64</th><th>int64</th><th>str3</th><th>int64</th><th>str4</th></tr></thead>\n",
       "<tr><td>001115.23+144601.8</td><td>2.8135</td><td>14.7672</td><td>0</td><td>GGG</td><td>3244</td><td>B600</td></tr>\n",
       "<tr><td>001115.23+144601.8</td><td>2.8135</td><td>14.7672</td><td>163</td><td>GGG</td><td>3244</td><td>R400</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "      SDSSJ        RA_GROUP DEC_GROUP GROUP_ID GROUP IGM_ID DISPERSER\n",
       "      str31        float64   float64   int64    str3 int64     str4  \n",
       "------------------ -------- --------- -------- ----- ------ ---------\n",
       "001115.23+144601.8   2.8135   14.7672        0   GGG   3244      B600\n",
       "001115.23+144601.8   2.8135   14.7672      163   GGG   3244      R400"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spec, meta = sdb.spectra_from_ID(3244)\n",
    "meta[['SDSSJ','RA_GROUP','DEC_GROUP','GROUP_ID', 'GROUP', 'IGM_ID', 'DISPERSER']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spectra from an input set of Coordinates in a group\n",
    "    This is only allowed for one group at a time.\n",
    "    It is risky in that only the first spectrum identified for each source is returned.\n",
    "    And it requires that *all* of the coordinates be found in the group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here is a example where each source has only 1 spectrum in the group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your search yielded 2 matches from 2 input coordinates\n",
      "Staged 2 spectra totalling 0.000186 Gb\n",
      "Loaded spectra\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<XSpectrum1D: file=none, nspec=2, select=0, wvmin=3605.79 Angstrom, wvmax=10287.3 Angstrom>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords = SkyCoord(ra=[0.0028, 0.0019], dec=[14.9747, 17.77374], unit='deg')\n",
    "spec, meta = sdb.spectra_in_group(coords, 'BOSS_DR12')\n",
    "spec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here is an example where each source has 2 spectra.  \n",
    "    Only the first found is returned. \n",
    "    In this case, the B600 grating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your search yielded 2 matches from 2 input coordinates\n",
      "Staged 2 spectra totalling 6.4e-05 Gb\n",
      "Loaded spectra\n",
      "Nspec = 2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=2&gt;\n",
       "<table id=\"table4691942224\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SDSSJ</th><th>RA_GROUP</th><th>DEC_GROUP</th><th>GROUP_ID</th><th>GROUP</th><th>IGM_ID</th><th>DISPERSER</th></tr></thead>\n",
       "<thead><tr><th>str31</th><th>float64</th><th>float64</th><th>int64</th><th>str3</th><th>int64</th><th>str4</th></tr></thead>\n",
       "<tr><td>001115.23+144601.8</td><td>2.813485</td><td>14.767169</td><td>0</td><td>GGG</td><td>3244</td><td>B600</td></tr>\n",
       "<tr><td>010619.24+004823.3</td><td>16.580172</td><td>0.806478</td><td>2</td><td>GGG</td><td>17656</td><td>B600</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=2>\n",
       "      SDSSJ         RA_GROUP DEC_GROUP GROUP_ID GROUP IGM_ID DISPERSER\n",
       "      str31         float64   float64   int64    str3 int64     str4  \n",
       "------------------ --------- --------- -------- ----- ------ ---------\n",
       "001115.23+144601.8  2.813485 14.767169        0   GGG   3244      B600\n",
       "010619.24+004823.3 16.580172  0.806478        2   GGG  17656      B600"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coords = SkyCoord(ra=[2.8135,16.5802], dec=[14.7672, 0.8065], unit='deg')\n",
    "spec, meta = sdb.spectra_in_group(coords, 'GGG')\n",
    "print(\"Nspec = {:d}\".format(spec.nspec))\n",
    "meta[['SDSSJ','RA_GROUP','DEC_GROUP','GROUP_ID', 'GROUP', 'IGM_ID', 'DISPERSER']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we request the other grating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Key DISPERSER in query_dict is not present in Table catalog\n",
      "Your search yielded 2 matches from 2 input coordinates\n",
      "Key flag_group in query_dict is not present in Table meta data\n",
      "Key flag_group in query_dict is not present in Table meta data\n",
      "Staged 2 spectra totalling 6.4e-05 Gb\n",
      "Loaded spectra\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=2&gt;\n",
       "<table id=\"table4680398736\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>SDSSJ</th><th>RA_GROUP</th><th>DEC_GROUP</th><th>GROUP_ID</th><th>GROUP</th><th>IGM_ID</th><th>DISPERSER</th></tr></thead>\n",
       "<thead><tr><th>str31</th><th>float64</th><th>float64</th><th>int64</th><th>str3</th><th>int64</th><th>str4</th></tr></thead>\n",
       "<tr><td>001115.23+144601.8</td><td>2.813485</td><td>14.767169</td><td>163</td><td>GGG</td><td>3244</td><td>R400</td></tr>\n",
       "<tr><td>010619.24+004823.3</td><td>16.580172</td><td>0.806478</td><td>165</td><td>GGG</td><td>17656</td><td>R400</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=2>\n",
       "      SDSSJ         RA_GROUP DEC_GROUP GROUP_ID GROUP IGM_ID DISPERSER\n",
       "      str31         float64   float64   int64    str3 int64     str4  \n",
       "------------------ --------- --------- -------- ----- ------ ---------\n",
       "001115.23+144601.8  2.813485 14.767169      163   GGG   3244      R400\n",
       "010619.24+004823.3 16.580172  0.806478      165   GGG  17656      R400"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qdict = dict(DISPERSER='R400')\n",
    "spec, meta = sdb.spectra_in_group(coords, 'GGG', query_dict=qdict)\n",
    "meta[['SDSSJ','RA_GROUP','DEC_GROUP','GROUP_ID', 'GROUP', 'IGM_ID', 'DISPERSER']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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